• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.848-857
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• 2021

This paper proposes an efficient approach based on a machine learning technique to predict the local stresses on mooring chain links. Three-link and multi-link finite element analyses were conducted for a target chain link of D107 with steel grade R4; 24,000 and 8000 analyses were performed, respectively. Two serial Artificial Neural Network (ANN) models based on a deep multi-layer perceptron technique were developed. The first ANN model corresponds to multi-link analyses, where the input neurons were the tension force and angle and the output neurons were the interlink angles. The second ANN model corresponds to the three-link analyses with the input neurons of the tension force, interlink angle, and the local stress positions, and the output neurons of the local stress. The predicted local stresses for the untrained cases were reliable compared to the numerical simulation results.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.25-32
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• 1995

In highly automated milling process, in-process monitoring of the malfunction is indispensable to ensure efficient cutting operation. Among many malfunctions in milling process, chatter vibration deteriorates surface finish, tool life and productivity. In this study, the monitoring system of chatter vibration for face milling process is proposed and experimentally estimated. The monitoring system employs two types of sensor such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are extracted in time domain for the input patterns of neural network to reduce time delay in signal processing state. The resultes of experimental evaluation show that the system works well over a wide range of cutting conditions.

• The Journal of Korean Academy of Prosthodontics
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• v.9 no.1
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• pp.63-68
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• 1969

The authors conducted the tests of the artificial masticatory forces on the everyday life foods divided into six groups of the vegetables, confectionaries, meats, fish, fruits and miscellaneous foods untreated or treated according to the taking of each foods by means of the human skull and electronic strain gauge for the pressure measurement. The results were as follows; 1. The masticatory forces of each foods were different. 2. The masticatory forces of the treated foods were lower than those of the untreated foods. 3. The masticatory forces needed to masticate the everyday life foods were considerably lower than the human maximum bite force. 4. It is reasonable to think the patients wearing well constructed full dentures feel no complications about the masticatory force for the mastication of everyday life foods. 5. The masticatory forces for the confectionaries were greater and more variable than the masticatory forces for the other foods.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.669-677
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• 2005

This acticle describes the control method of mobile robots for avoiding slip and turnover on sloped terrain. An inexpensive gyro/vision sensor module is suggested for obtaining the information of terrain at present and future. Using the terrain information and the robot state, the maximum limit velocity of the forward velocity of the robot is defined fur avoiding slip and turnover of the robot. Simultaneously the maximum value of the robot velocity is reflected to an operator in the form of reflective force on a forte feedback joystick. Consequently the operator can recognize the maximum velocity of the robot determined by the terrain information and the robot state. In this point of view, the inconsistency of the robot movement and the user's command caused by the limit velocity of the robot can be compensated by the reflective force. The experimenal results show the effectiveness of the suggested method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.71-75
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• 1993

The application of artificial neural network forcondition monitoring and diagnosis of milling tools was introduced. To detect the conditions of milling tools, the monitoring wywtem consists of three phases: "preparation phase", "learning phase", "production phase". The conditions of milling tools were categorized into the three states. "normal", "warning", "abnormal". The dectection of tool condition, in this paper, could be successfully performed by monitoring the variation of power spectrum on Y dirctional cutting force.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.827-828
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• 2009

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.4
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• pp.8-15
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• 2011

This paper proposes a real-time navigation algorithm which integrates the artificial potential field (APF) for an unmanned vehicle in the unknown environment. This approach uses repulsive potential function around the obstacles to force the vehicle away and an attractive potential function around the goal to attract the vehicle. In this research, laser range finder is used as range sensor. An obstacle detected by the sensor creates repulsive vector. Differential global positioning system (DGPS) and digital compass are used to measure the current vehicle position and orientation. The measured vehicle position is also used to create attractive vector. This paper proposes a new concept of potential field based navigation which controls unmanned vehicle's speed and steering. The magnitude of repulsive force based on the proposed algorithm is designed not to be over the magnitude of attractive force while the magnitude is increased linearly as being closer to obstacle. Consequently, the vehicle experiences a generalized force toward the negative gradient of the total potential. This force drives the vehicle downhill towards its goal configuration until the vehicle reaches minimum potential and it stops. The effectiveness of the proposed APF for unmanned vehicle is verified through simulation and experiment.

• Journal of the Korean Society of Safety
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• v.12 no.3
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• pp.178-184
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• 1997

Many mathematical techniques have been developed to determine the muscle forces and force distribution in biomechanical human model, because it is so important to understand internal forces resisting external loading. However, a three-dimensional mathematical model of wrist joint, which is essential to develop solid modeling and artificial wrist joint, has not been well developed. This study proposed to define three-dimensional mathematical model of distal radius and ulna of the human wrist and to develop a detailed two-dimensional finite element through comparisons to existing analytical models and experimental tests. This mathematical model were accurately recreated, allowing the internal tendon force as well as force transmission and distribution through the distal radios and ulna during dynamic loadings. The results found in this study indicate and support the findings of other investigator that cyclic loading condition results in higher compression force on distal radius and ulna and may be source of wrist disorder.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.172-177
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• 2013

With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.

• Structural Engineering and Mechanics
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• v.27 no.2
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• pp.117-134
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• 2007

The force (load) reduction factor, R, which is one of the most important parameters in earthquake load calculation, is independent of the dimensions of the structure but is defined on the basis of the load bearing system of the structure as defined in earthquake codes. Significant damages and failures were experienced on prefabricated reinforced concrete structures during the last three major earthquakes in Turkey (Adana 1998, Kocaeli 1999, Duzce 1999) and the experts are still discussing the main reasons of those failures. Most of them agreed that they resulted mainly from the earthquake force reduction factor, R that is incorrectly selected during design processes, in addition to all other detailing errors. Thus this wide spread damages caused by the earthquake to prefabricated structures aroused suspicion about the correctness of the R coefficient recommended in the current Turkish Earthquake Codes (TEC - 98). In this study, an attempt was made for an approximate determination of R coefficient for widely utilized prefabricated structure types (single-floor single-span) with variable dimensions. According to the selecting variable dimensions, 140 sample frames were computed using pushover analysis. The force reduction factor R was calculated by load-displacement curves obtained pushover analysis for each frame. Then, formulated artificial neural network method was trained by using 107 of the 140 sample frames. For the training various algorithms were used. The method was applied and used for the prediction of the R rest 33 frames with about 92% accuracy. The paper also aims at proposing the authorities to change the R coefficient values predicted in TEC - 98 for prefabricated concrete structures.